import os, sys, shutil from typing import List, Optional, Tuple, Union from pathlib import Path import csv import random import numpy as np import ffmpeg import json import imageio import collections import cv2 import pdb import math import PIL.Image as Image csv.field_size_limit(13107200) # Default setting is 131072, 100x expand should be enough import torch from torch.utils.data import Dataset from torchvision import transforms # Import files from the local folder root_path = os.path.abspath('.') sys.path.append(root_path) from utils.optical_flow_utils import flow_to_image, filter_uv, bivariate_Gaussian # Init paramter and global shared setting # Blurring Kernel blur_kernel = bivariate_Gaussian(45, 3, 3, 0, grid = None, isotropic = True) # Color all_color_codes = [(255, 0, 0), (255, 255, 0), (0, 255, 0), (0, 255, 255), (255, 0, 255), (0, 0, 255), (128, 128, 128), (64, 224, 208), (233, 150, 122)] for _ in range(100): # Should not be over 100 colors all_color_codes.append((random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))) # Data Transforms train_transforms = transforms.Compose( [ transforms.Lambda(lambda x: x / 255.0 * 2.0 - 1.0), ] ) class VideoDataset_Motion_FrameINO(Dataset): def __init__( self, config, csv_folder_path, FrameOut_only = False, one_point_one_obj = False, strict_validation_match = False, ) -> None: super().__init__() # Fetch the Fundamental Setting self.dataset_folder_path = config["dataset_folder_path"] if not FrameOut_only: # Frame In mode self.ID_folder_path = config["ID_folder_path"] self.target_height = config["height"] self.target_width = config["width"] # self.ref_cond_size = config["ref_cond_size"] self.preset_decode_fps = config["preset_decode_fps"] # Set to be 16 self.train_frame_num = config["train_frame_num"] self.empty_text_prompt = config["empty_text_prompt"] self.start_skip = config["start_skip"] self.end_skip = config["end_skip"] self.dot_radius = int(config["dot_radius"]) # Set to be 6 self.point_keep_ratio_ID = config["point_keep_ratio_ID"] self.point_keep_ratio_regular = config["point_keep_ratio_regular"] self.faster_motion_prob = config["faster_motion_prob"] self.FrameOut_only = FrameOut_only self.one_point_one_obj = one_point_one_obj # Currently, this only open when FrameOut_only = True self.strict_validation_match = strict_validation_match self.config = config # Sanity Check assert(self.point_keep_ratio_ID <= 1.0) assert(self.point_keep_ratio_regular <= 1.0) # Read the CSV files info_lists = [] for csv_file_name in os.listdir(csv_folder_path): # Read all csv files csv_file_path = os.path.join(csv_folder_path, csv_file_name) with open(csv_file_path) as file_obj: reader_obj = csv.reader(file_obj) # Iterate over each row in the csv for idx, row in enumerate(reader_obj): if idx == 0: elements = dict() for element_idx, key in enumerate(row): elements[key] = element_idx continue # Read the important information info_lists.append(row) # Organize self.info_lists = info_lists self.element_idx_dict = elements # Log print("The number of videos for ", csv_folder_path, " is ", len(self.info_lists)) # print("The memory cost is ", sys.getsizeof(self.info_lists)) def __len__(self): return len(self.info_lists) @staticmethod def prepare_traj_tensor(full_pred_tracks, original_height, original_width, selected_frames, dot_radius, target_width, target_height, region_box, idx = 0, first_frame_img = None): # Prepare the color and other stuff target_color_codes = all_color_codes[:len(full_pred_tracks[0])] # This means how many objects in total we have (top_left_x, top_left_y), (bottom_right_x, bottom_right_y) = region_box # Prepare the traj image traj_img_lists = [] # Set a new dot radius based on the resolution fluctuating dot_radius_resize = int( dot_radius * original_height / 384 ) # This is set with respect to default 384 height, will be adjust based on the height change # Prepare base draw image if there is if first_frame_img is not None: img_with_traj = first_frame_img.copy() # Iterate all object instance merge_frames = [] for temporal_idx, obj_points in enumerate(full_pred_tracks): # Iterate all downsampled frames, should be 13 # Init the base img for the traj figures base_img = np.zeros((original_height, original_width, 3)).astype(np.float32) # Use the original image size base_img.fill(255) # Whole white frames # Iterate for the per object for obj_idx, points in enumerate(obj_points): # Basic setting color_code = target_color_codes[obj_idx] # Color across frames should be consistent # Process all points in this current object for (horizontal, vertical) in points: if horizontal < 0 or horizontal >= original_width or vertical < 0 or vertical >= original_height: continue # If the point is already out of the range, Don't draw # Draw square around the target position vertical_start = min(original_height, max(0, vertical - dot_radius_resize)) vertical_end = min(original_height, max(0, vertical + dot_radius_resize)) # Diameter, used to be 10, but want smaller if there are too many points now horizontal_start = min(original_width, max(0, horizontal - dot_radius_resize)) horizontal_end = min(original_width, max(0, horizontal + dot_radius_resize)) # Paint base_img[vertical_start:vertical_end, horizontal_start:horizontal_end, :] = color_code # Draw the visual of traj if needed if first_frame_img is not None: img_with_traj[vertical_start:vertical_end, horizontal_start:horizontal_end, :] = color_code # Resize frames Don't use negative and don't resize in [0,1] base_img = cv2.resize(base_img, (target_width, target_height), interpolation = cv2.INTER_CUBIC) # Dilate (Default to be True) base_img = cv2.filter2D(base_img, -1, blur_kernel).astype(np.uint8) # Append selected_frames and the color together for visualization merge_frame = selected_frames[temporal_idx].copy() merge_frame = cv2.rectangle(merge_frame, (top_left_x, top_left_y), (bottom_right_x, bottom_right_y), (255, 0, 0), 5) # Draw the Region Box Area merge_frame[base_img < 250] = base_img[base_img < 250] merge_frames.append(merge_frame) # Append to the temporal index traj_img_lists.append(base_img) # Convert to tensor traj_imgs_np = np.array(traj_img_lists) traj_tensor = torch.tensor(traj_imgs_np) # Transform traj_tensor = traj_tensor.float() traj_tensor = torch.stack([train_transforms(traj_frame) for traj_frame in traj_tensor], dim=0) traj_tensor = traj_tensor.permute(0, 3, 1, 2).contiguous() # [F, C, H, W] # Write to video (For Debug Purpose) # imageio.mimsave("merge_cond" + str(idx) + ".mp4", merge_frames, fps=12) # Return merge_frames = np.array(merge_frames) if first_frame_img is not None: return traj_tensor, traj_imgs_np, merge_frames, img_with_traj else: return traj_tensor, traj_imgs_np, merge_frames # Need to return traj_imgs_np for other purpose def __getitem__(self, idx): while True: # Iterate until there is a valid video read try: # Fetch the information info = self.info_lists[idx] video_path = os.path.join(self.dataset_folder_path, info[self.element_idx_dict["video_path"]]) original_height = int(info[self.element_idx_dict["height"]]) original_width = int(info[self.element_idx_dict["width"]]) num_frames = int(info[self.element_idx_dict["num_frames"]]) fps = float(info[self.element_idx_dict["fps"]]) # Fetch all panoptic frames FrameIN_info_all = json.loads(info[self.element_idx_dict["FrameIN_info"]]) Track_Traj_all = json.loads(info[self.element_idx_dict["Track_Traj"]]) text_prompt_all = json.loads(info[self.element_idx_dict["Improved_Text_Prompt"]]) ID_info_all = json.loads(info[self.element_idx_dict["ID_info"]]) # Randomly Choose one available panoptic_idx = random.choice(range(len(FrameIN_info_all))) FrameIN_info = FrameIN_info_all[panoptic_idx] Track_Traj = Track_Traj_all[panoptic_idx] text_prompt = text_prompt_all[panoptic_idx] ID_info_panoptic = ID_info_all[panoptic_idx] # Organize resolution = str(self.target_width) + "x" + str(self.target_height) fps_scale = self.preset_decode_fps / fps downsample_num_frames = int(num_frames * fps_scale) # FrameIn drop if self.FrameOut_only or random.random() < self.config["drop_FrameIn_prob"]: drop_FrameIn = True else: drop_FrameIn = False # Sanity check if not os.path.exists(video_path): raise Exception("This video path ", video_path, " doesn't exists!") # Not all objects is ideal FrameIn, we need to select if not self.strict_validation_match: effective_obj_idxs = [] for obj_idx, obj_info in enumerate(ID_info_panoptic): if obj_info != []: effective_obj_idxs.append(obj_idx) main_target_obj_idx = random.choice(effective_obj_idxs) # NOTE: I think we should only has one object to be processed for now else: main_target_obj_idx = 0 # Always choose the first one #################################################### Fetch FrameIn ID information ############################################################### # Fetch the FrameIn ID info segmentation_info, useful_region_box = ID_info_panoptic[main_target_obj_idx] # There might be multiple objects ideal, but we just randomly choose one if not self.FrameOut_only: _, first_frame_reference_path, _ = segmentation_info # bbox_info, first_frame_reference_path, store_img_path_lists first_frame_reference_path = os.path.join(self.ID_folder_path, first_frame_reference_path) ################################################################################################################################################## ############ Randomly choose one mask inside the multiple choice available (Resolution is respect to the origional resolution) ############ useful_region_box.sort(key=lambda x: x[0]) # Choose one region box if not self.strict_validation_match: mask_region = random.choice(useful_region_box[-5:])[1:] # Choose in the largest 5 available else: mask_region = useful_region_box[-1][1:] # Choose the last one # Fetch (top_left_x_raw, top_left_y_raw), (bottom_right_x_raw, bottom_right_y_raw) = mask_region # As Original Resolution # Resize the mask based on the CURRENT Target resolution (现在的384x480的resolution了) top_left_x = int(top_left_x_raw * self.target_width / original_width) top_left_y = int(top_left_y_raw * self.target_height / original_height) bottom_right_x = int(bottom_right_x_raw * self.target_width / original_width) bottom_right_y = int(bottom_right_y_raw * self.target_height / original_height) resized_mask_region_box = (top_left_x, top_left_y), (bottom_right_x, bottom_right_y) ########################################################################################################################################### ############################################## Read the video by ffmpeg ############################################################# # Read the video by ffmpeg in the needed decode fps and resolution video_stream, err = ffmpeg.input( video_path ).filter( 'fps', fps = self.preset_decode_fps, round = 'up' ).output( "pipe:", format = "rawvideo", pix_fmt = "rgb24", s = resolution ).run( capture_stdout = True, capture_stderr = True ) # The resize is already included video_np_raw = np.frombuffer(video_stream, np.uint8).reshape(-1, self.target_height, self.target_width, 3) # Sanity Check if len(video_np_raw) - self.start_skip - self.end_skip < self.train_frame_num: raise Exception("The number of frames from the video is not enough") # Crop the tensor with all Non-interest region becomes blank(black-0 value); The region is target resolution in training with VAE step size adjustment video_np_masked = np.zeros(video_np_raw.shape, dtype = np.uint8) video_np_masked[:, top_left_y:bottom_right_y, top_left_x:bottom_right_x, :] = video_np_raw[:, top_left_y:bottom_right_y, top_left_x:bottom_right_x, :] ######################################################################################################################################### ######################################### Define the text prompt ####################################################### # Whether empty text prompt; Text Prompt already exists above if self.empty_text_prompt or random.random() < self.config["text_mask_ratio"]: text_prompt = "" ######################################################################################################################## ###################### Prepare the Tracking points for each object (each object has different color) ################################# # Make sure that the frame from the FrameIN_info has enough number of frames _, original_start_frame_idx, fps_scale = FrameIN_info[main_target_obj_idx] # This is expected to be all the same in the video downsample_start_frame_idx = max(0, int(original_start_frame_idx * fps_scale)) # Check the max number of frames available (NOTE: Recommended to use Full Text Prompt Version) max_step_num = (downsample_num_frames - downsample_start_frame_idx) // self.train_frame_num if max_step_num == 0: print("This video is ", video_path) raise Exception("The video is too short!") elif max_step_num >= 2 and random.random() < self.faster_motion_prob: iter_gap = 2 # Maximum Setting now is 2x; else, the VAE might not works well else: iter_gap = 1 # Iterate all the Segmentation Info full_pred_tracks = [[] for _ in range(self.train_frame_num)] # The dim should be: (temporal, object, points, xy) The fps should be fixed to 12 fps, which is the same as training decode fps # Iterate all objects but not the main objects for obj_idx in range(len(ID_info_panoptic)): # Prepare the tracjectory pred_tracks = Track_Traj[obj_idx] pred_tracks = pred_tracks[downsample_start_frame_idx : downsample_start_frame_idx + iter_gap * self.train_frame_num : iter_gap] if len(pred_tracks) != self.train_frame_num: raise Exception("The len of pre_track does not match") # For Non-main obj idx, we must ensure all points inside the region box; If it is main obj, the ID must be outside the region box if obj_idx != main_target_obj_idx or self.FrameOut_only: # Randomly select the points based on the prob given, here, the number of points is different for each objeects kept_point_status = random.choices([True, False], weights = [self.point_keep_ratio_regular, 1 - self.point_keep_ratio_regular], k = len(pred_tracks[0])) # Check witht the first frame, No need to check for following frames (allowed to have FrameOut effect) first_frame_points = pred_tracks[0] for point_idx in range(len(first_frame_points)): (horizontal, vertical) = first_frame_points[point_idx] if horizontal < top_left_x_raw or horizontal >= bottom_right_x_raw or vertical < top_left_y_raw or vertical >= bottom_right_y_raw: kept_point_status[point_idx] = False else: # For main object # Randomly select the points based on the prob given, here, the number of points is different for each objeects if drop_FrameIn: # No motion provided on ID for Drop FrameIn cases kept_point_status = random.choices([False], k = len(pred_tracks[0])) else: # Regular FrameIn case kept_point_status = random.choices([True, False], weights = [self.point_keep_ratio_ID, 1 - self.point_keep_ratio_ID], k = len(pred_tracks[0])) # Sanity Check if len(kept_point_status) != len(pred_tracks[-1]): raise Exception("The number of points filterred is not match with the dataset") # Iterate and add all temporally for temporal_idx, pred_track in enumerate(pred_tracks): # Iterate all point one by one left_points = [] for point_idx in range(len(pred_track)): # Select kept points if kept_point_status[point_idx]: left_points.append(pred_track[point_idx]) # Append the left points to the list full_pred_tracks[temporal_idx].append(left_points) # pred_tracks will be 49 frames, and each one represent all tracking points for single objects; only one object here # Fetch One Point if self.one_point_one_obj: one_track_point = [] for full_pred_track_per_frame in full_pred_tracks: one_track_point.append( [[full_pred_track_per_frame[0][0]]]) ####################################################################################################################################### ############################### Process the Video Tensor (based on info fetched from traj) ############################################ # Select Frames based on the panoptic range (No Mask here) selected_frames = video_np_raw[downsample_start_frame_idx : downsample_start_frame_idx + iter_gap * self.train_frame_num : iter_gap] # Prepare the Video Tensor; NOTE: in this branch, video tensor is full image without mask video_tensor = torch.tensor(selected_frames) # Convert to tensor if len(video_tensor) != self.train_frame_num: raise Exception("The len of train frames does not match") # Training transforms for the Video and condition video_tensor = video_tensor.float() video_tensor = torch.stack([train_transforms(frame) for frame in video_tensor], dim=0) video_tensor = video_tensor.permute(0, 3, 1, 2).contiguous() # [F, C, H, W] if drop_FrameIn: main_reference_img = np.uint8(np.zeros((self.target_height, self.target_width, 3))) # Whole Black (0-value) pixel placeholder else: # Fetch the reference and resize main_reference_img = np.asarray(Image.open(first_frame_reference_path)) # Resize to the same size as the video ref_h, ref_w = main_reference_img.shape[:2] scale_h = self.target_height / max(ref_h, ref_w) scale_w = self.target_width / max(ref_h, ref_w) new_h, new_w = int(ref_h * scale_h), int(ref_w * scale_w) main_reference_img = cv2.resize(main_reference_img, (new_w, new_h), interpolation = cv2.INTER_AREA) # Calculate padding amounts on all direction pad_height1 = (self.target_height - main_reference_img.shape[0]) // 2 pad_height2 = self.target_height - main_reference_img.shape[0] - pad_height1 pad_width1 = (self.target_width - main_reference_img.shape[1]) // 2 pad_width2 = self.target_width - main_reference_img.shape[1] - pad_width1 # Apply padding to same resolution as the training farmes main_reference_img = np.pad( main_reference_img, ((pad_height1, pad_height2), (pad_width1, pad_width2), (0, 0)), mode = 'constant', constant_values = 0 ) # cv2.imwrite("main_reference_img_padded"+str(idx)+".png", cv2.cvtColor(main_reference_img, cv2.COLOR_BGR2RGB)) # Convert to tensor main_reference_tensor = torch.tensor(main_reference_img) main_reference_tensor = train_transforms(main_reference_tensor).permute(2, 0, 1).contiguous() # Fetch the first frame and then do ID merge for this branch of training first_frame_np = video_np_masked[downsample_start_frame_idx] # Needs to return for Validation # cv2.imwrite("first_frame"+str(idx)+".png", cv2.cvtColor(first_frame_np, cv2.COLOR_BGR2RGB)) # Convert to Tensor and then Transforms first_frame_tensor = torch.tensor(first_frame_np) first_frame_tensor = train_transforms(first_frame_tensor).permute(2, 0, 1).contiguous() ####################################################################################################################################### ############################################## Draw the Traj Points and Transform to Tensor ############################################# # Draw the dilated points if self.one_point_one_obj: target_pred_tracks = one_track_point # For this case, we only has one point per one object else: target_pred_tracks = full_pred_tracks traj_tensor, traj_imgs_np, merge_frames = self.prepare_traj_tensor(target_pred_tracks, original_height, original_width, selected_frames, self.dot_radius, self.target_width, self.target_height, resized_mask_region_box, idx) ######################################################################################################################################### # Write some processed meta data processed_meta_data = { "full_pred_tracks": full_pred_tracks, "original_width": original_width, "original_height": original_height, "mask_region": mask_region, "resized_mask_region_box": resized_mask_region_box, } except Exception as e: print("The exception is ", e) old_idx = idx idx = random.randint(0, len(self.info_lists)) print("We cannot process the video", old_idx, " and we choose a new idx of ", idx) continue # For any error occurs, we run it again with new idx proposed (a random int less than current value) # If everything is ok, we should break at the end break # Return the information return { "video_tensor": video_tensor, "traj_tensor": traj_tensor, "first_frame_tensor": first_frame_tensor, "main_reference_tensor": main_reference_tensor, "text_prompt": text_prompt, # The rest are auxiliary data for the validation/testing purposes "video_gt_np": selected_frames, "first_frame_np": first_frame_np, "main_reference_np": main_reference_img, "processed_meta_data": processed_meta_data, "traj_imgs_np": traj_imgs_np, "merge_frames" : merge_frames, "gt_video_path": video_path, }